Final Project Rough Draft
library(mosaic)
library(tidyverse)
library(lubridate)
library(DataComputing)
library(rvest)
library(broom)Guiding Question (i.e., Research Question):
How do different aspects of determining how developed a country is (such as birth rate, population, death rate, etc.) manifest themselves in the spread of COVID-19. Essentially, how does COVID-19 show different levels of impact on different countries?
Data Access
Reading in the Data:
Data Source 1: COVID
COVID <- read.csv(file = "total-covid-cases-deaths-per-million.csv")
COVIDCOVID %>%
nrow()[1] 9487COVID %>%
names() [1] "total.covid.cases.deaths.per.million" "X"
[3] "X.1" "X.2"
[5] "X.3" "X.4"
[7] "X.5" "X.6"
[9] "X.7" "X.8"
[11] "X.9" "X.10"
[13] "X.11" "X.12"
[15] "X.13" "X.14"
[17] "X.15" "X.16"
[19] "X.17" "X.18"
[21] "X.19" "X.20"
[23] "X.21" "X.22"
[25] "X.23" "X.24"
[27] "X.25" "X.26"
[29] "X.27" "X.28"
[31] "X.29" "X.30"
[33] "X.31" "X.32"
[35] "X.33" "X.34"
[37] "X.35" "X.36"
[39] "X.37" "X.38"
[41] "X.39" "X.40"
[43] "X.41" "X.42"
[45] "X.43" "X.44"
[47] "X.45" "X.46"
[49] "X.47" "X.48"
[51] "X.49" "X.50"
[53] "X.51" "X.52"
[55] "X.53" "X.54"
[57] "X.55" "X.56"
[59] "X.57" "X.58"
[61] "X.59" "X.60"
[63] "X.61" "X.62"
[65] "X.63" "X.64"
[67] "X.65" "X.66"
[69] "X.67" "X.68"
[71] "X.69" "X.70"
[73] "X.71" "X.72"
[75] "X.73" "X.74"
[77] "X.75" "X.76"
[79] "X.77" "X.78"
[81] "X.79" "X.80"
[83] "X.81" "X.82"
[85] "X.83" "X.84"
[87] "X.85" "X.86"
[89] "X.87" "X.88"
[91] "X.89" "X.90"
[93] "X.91" "X.92"
[95] "X.93" "X.94"
[97] "X.95" "X.96"
[99] "X.97" "X.98"
[101] "X.99" "X.100"
[103] "X.101" "X.102"
[105] "X.103" "X.104"
[107] "X.105" "X.106"
[109] "X.107" "X.108"
[111] "X.109" "X.110"
[113] "X.111" "X.112"
[115] "X.113" "X.114"
[117] "X.115" "X.116"
[119] "X.117" "X.118"
[121] "X.119" "X.120"
[123] "X.121" "X.122"
[125] "X.123" "X.124"
[127] "X.125" "X.126"
[129] "X.127" "X.128"
[131] "X.129" "X.130"
[133] "X.131" "X.132"
[135] "X.133" "X.134"
[137] "X.135" "X.136"
[139] "X.137" "X.138"
[141] "X.139" "X.140"
[143] "X.141" "X.142"
[145] "X.143" "X.144"
[147] "X.145" "X.146"
[149] "X.147" "X.148"
[151] "X.149" "X.150"
[153] "X.151" "X.152"
[155] "X.153" "X.154"
[157] "X.155" "X.156"
[159] "X.157" "X.158"
[161] "X.159" "X.160"
[163] "X.161" "X.162"
[165] "X.163" "X.164"
[167] "X.165" "X.166"
[169] "X.167" "X.168"
[171] "X.169" "X.170"
[173] "X.171" "X.172"
[175] "X.173" "X.174"
[177] "X.175" "X.176"
[179] "X.177" "X.178"
[181] "X.179" "X.180"
[183] "X.181" "X.182"
[185] "X.183" "X.184"
[187] "X.185" "X.186"
[189] "X.187" "X.188"
[191] "X.189" "X.190"
[193] "X.191" "X.192"
[195] "X.193" "X.194"
[197] "X.195" "X.196"
[199] "X.197" "X.198"
[201] "X.199" "X.200"
[203] "X.201" "X.202"
[205] "X.203" "X.204"
[207] "X.205" "X.206"
[209] "X.207" "X.208"
[211] "X.209" "X.210"
[213] "X.211" "X.212"
[215] "X.213" "X.214"
[217] "X.215" "X.216"
[219] "X.217" "X.218"
[221] "X.219" "X.220"
[223] "X.221" "X.222"
[225] "X.223" "X.224"
[227] "X.225" "X.226"
[229] "X.227" "X.228"
[231] "X.229" "X.230"
[233] "X.231" "X.232"
[235] "X.233" "X.234"
[237] "X.235" "X.236"
[239] "X.237" "X.238"
[241] "X.239" "X.240"
[243] "X.241" "X.242"
[245] "X.243" "X.244"
[247] "X.245" "X.246"
[249] "X.247" "X.248"
[251] "X.249" "X.250"
[253] "X.251" "X.252"
[255] "X.253" "X.254" COVID %>%
head()Data Source 2: CountryData
CountryDataCountryData %>%
nrow()[1] 256CountryData %>%
names() [1] "country" "area" "pop" "growth" "birth"
[6] "death" "migr" "maternal" "infant" "life"
[11] "fert" "health" "HIVrate" "HIVpeople" "HIVdeath"
[16] "obesity" "underweight" "educ" "unemploymentYouth" "GDP"
[21] "GDPgrowth" "GDPcapita" "saving" "indProd" "labor"
[26] "unemployment" "family" "tax" "budget" "debt"
[31] "inflation" "discount" "lending" "narrow" "broad"
[36] "credit" "shares" "balance" "exports" "imports"
[41] "gold" "externalDebt" "homeStock" "abroadStock" "elecProd"
[46] "elecCons" "elecExp" "elecImp" "elecCap" "elecFossil"
[51] "elecNuc" "elecHydro" "elecRenew" "oilProd" "oilExp"
[56] "oilImp" "oilRes" "petroProd" "petroCons" "petroExp"
[61] "petroImp" "gasProd" "gasCons" "gasExp" "gasImp"
[66] "gasRes" "mainlines" "cell" "netHosts" "netUsers"
[71] "airports" "railways" "roadways" "waterways" "marine"
[76] "military" CountryData %>%
head()Data Source 3: Continents
Continents <- read.csv(file = "countries and continents.csv")
ContinentsContinents %>%
nrow()[1] 251Continents %>%
names() [1] "name" "official_name_en" "official_name_fr"
[4] "ISO3166.1.Alpha.2" "ISO3166.1.Alpha.3" "M49"
[7] "ITU" "MARC" "WMO"
[10] "DS" "Dial" "FIFA"
[13] "FIPS" "GAUL" "IOC"
[16] "ISO4217.currency_alphabetic_code" "ISO4217.currency_country_name" "ISO4217.currency_minor_unit"
[19] "ISO4217.currency_name" "ISO4217.currency_numeric_code" "is_independent"
[22] "Capital" "Continent" "TLD"
[25] "Languages" "Geoname.ID" "EDGAR" Continents %>%
head()Data Wrangling of COVID Dataset
COVIDTidyCOVID <- COVID %>%
rename(country = total.covid.cases.deaths.per.million ) %>%
rename( Code = X ) %>%
rename(Date = X.1 ) %>%
rename(DeathsPerMillion = X.2) %>%
rename(CasesPerMillion = X.3) %>%
filter(row_number() > 1) %>%
subset(select = c(1,2,3,4,5)) %>%
mutate( country = as.character(country) ) %>%
mutate( Code = as.character(Code) ) %>%
mutate(Date = mdy(Date)) %>%
mutate(DeathsPerMillion = as.integer(DeathsPerMillion) - 1) %>%
mutate(CasesPerMillion = as.integer(CasesPerMillion) - 1)
TidyCOVIDTidyCOVIDFebruary1 <-
TidyCOVID %>%
filter(Date == "2020-02-01") %>%
subset(select = c(1,4,5)) %>%
rename(DeathsPerMillionFeb1 = DeathsPerMillion) %>%
rename(CasesPerMillionFeb1 = CasesPerMillion)
February29 <-
TidyCOVID %>%
filter(Date == "2020-02-29") %>%
subset(select = c(1,4,5)) %>%
rename(DeathsPerMillionFeb29 = DeathsPerMillion) %>%
rename(CasesPerMillionFeb29 = CasesPerMillion)
FebGrow <-
full_join(February1, February29, by = c("country")) %>%
mutate("CasesPerMillionFebGrowth" = CasesPerMillionFeb29 - CasesPerMillionFeb1) %>%
mutate("DeathsPerMillionFebGrowth" = DeathsPerMillionFeb29 - DeathsPerMillionFeb1)
March1 <-
TidyCOVID %>%
filter(Date == "2020-03-01") %>%
subset(select = c(1,4,5)) %>%
rename(DeathsPerMillionMarch1 = DeathsPerMillion) %>%
rename(CasesPerMillionMarch1 = CasesPerMillion)
March31 <-
TidyCOVID %>%
filter(Date == "2020-03-31") %>%
subset(select = c(1,4,5)) %>%
rename(DeathsPerMillionMarch31 = DeathsPerMillion) %>%
rename(CasesPerMillionMarch31 = CasesPerMillion)
MarchGrow <-
full_join(March1, March31, by = c("country")) %>%
mutate("CasesPerMillionMarchGrowth" = CasesPerMillionMarch31 - CasesPerMillionMarch1) %>%
mutate("DeathsPerMillionMarchGrowth" = DeathsPerMillionMarch31 - DeathsPerMillionMarch1)
December31 <-
TidyCOVID %>%
filter(Date == "2019-12-31") %>%
subset(select = c(1,4,5)) %>%
rename(DeathsPerMillionDecember31 = DeathsPerMillion) %>%
rename(CasesPerMillionDecember31 = CasesPerMillion)
April5 <-
TidyCOVID %>%
filter(Date == "2020-04-05") %>%
subset(select = c(1,4,5)) %>%
rename(DeathsPerMillionApril5 = DeathsPerMillion) %>%
rename(CasesPerMillionApril5 = CasesPerMillion)
TotalGrow <-
full_join(December31, April5, by = c("country")) %>%
mutate("CasesPerMillionTotalGrowth" = CasesPerMillionApril5 - CasesPerMillionDecember31) %>%
mutate("DeathsPerMillionTotalGrowth" = DeathsPerMillionApril5 - DeathsPerMillionDecember31)FebGrow <-
FebGrow %>%
subset(select = c(1, 6, 7))
MarchGrow <-
MarchGrow %>%
subset(select = c(1, 6, 7))
TotalGrow <-
TotalGrow %>%
subset(select = c(1, 6, 7))
Growth <-
left_join(FebGrow,MarchGrow) %>%
left_join(TotalGrow)Joining, by = "country"
Joining, by = "country"GrowthData Wrangling of Continents
ContinentsTidyCont <-
Continents %>%
filter(row_number() > 2) %>%
subset(select = c(1,23)) %>%
rename(country = name ) %>%
mutate( country = as.character(country) ) %>%
mutate( Continent = as.character(Continent) )
TidyContCOVIDwCont <-
left_join(TidyCOVID, TidyCont, by = "country")Data Visualization
DataMix <- left_join(COVIDwCont, CountryData, by = "country")JOEY: I think this is a good one to start out with, but if there is some way that you could make this more clear (color code, etc.) that would be great. This is a good start to our data.
ggplot(data = DataMix,aes(x = Date, y = CasesPerMillion)) +
geom_point() +
facet_wrap(~Continent)
JOEY: I like this graph because it visualizes one of the factors that contributes to the development classification. Can we add in a color code or something that would represent one more factor, such as different countries or regions of the world. The more that we can do with this one, the better.
ggplot(data= DataMix, aes(x= Date, y = DeathsPerMillion)) +
geom_point() +
facet_wrap(~Continent)
GrowthMix <-left_join(Growth, CountryData, by = "country")ggplot(data=GrowthMix, aes(x = airports,y = CasesPerMillionTotalGrowth)) +
geom_point() +
xlim(0,5000)
ggplot(data=GrowthMix, aes(x = health,y = CasesPerMillionTotalGrowth)) +
geom_point()
ggplot(data=GrowthMix, aes(x = health,y = DeathsPerMillionTotalGrowth)) +
geom_point()
GrowthMix %>%
arrange(desc(CasesPerMillionTotalGrowth))ShowChange <-
GrowthMix %>%
select(c(1, 6, 7, 9)) %>%
filter(country != "Africa",
country != "Asia",
country != "Europe",
country != "North America",
country != "Oceania",
country != "South America",
country != "World"
) %>%
mutate("TotalGrowth" = (CasesPerMillionTotalGrowth * round(pop/1000000, digits = 0))) %>%
mutate("TotalDeath" = (DeathsPerMillionTotalGrowth * round(pop/1000000, digits = 0)))
ShowChange %>%
arrange(desc(TotalDeath))WorldMap(ShowChange, key = country, fill = TotalGrowth)Mapping API still under development and may change in future releases.
---
title: "Final Project Rough Draft"
output: html_notebook
authors: "Evelyn Murray and Joseph Pevner"
---

```{r}
library(mosaic)
library(tidyverse)
library(lubridate)
library(DataComputing)
library(rvest)
library(broom)
```

## Guiding Question (i.e., Research Question):

How do different aspects of determining how developed a country is (such as birth rate, population, death rate, etc.) manifest themselves in the spread of COVID-19.  Essentially, how does COVID-19 show different levels of impact on different countries?


## Data Access

Reading in the Data:


Data Source 1: COVID
```{r}
COVID <- read.csv(file = "total-covid-cases-deaths-per-million.csv")
COVID
```

```{r}
COVID %>%
  nrow()
```
```{r}
COVID %>%
  names()
```
```{r}
COVID %>%
  head()
```



Data Source 2: CountryData
```{r}
CountryData
```

```{r}
CountryData %>%
  nrow()
```
```{r}
CountryData %>%
  names()
```
```{r}
CountryData %>%
  head()
```



Data Source 3: Continents
```{r}
Continents <- read.csv(file = "countries and continents.csv")
Continents
```
```{r}
Continents %>%
  nrow()
```
```{r}
Continents %>%
  names()
```
```{r}
Continents %>%
  head()
```




### Data Wrangling of COVID Dataset

```{r}
COVID
```


```{r}
TidyCOVID <- COVID %>%
  rename(country = total.covid.cases.deaths.per.million ) %>%
  rename( Code = X ) %>%
  rename(Date = X.1 ) %>%
  rename(DeathsPerMillion = X.2) %>%
  rename(CasesPerMillion = X.3) %>%
  filter(row_number() > 1) %>%
  subset(select = c(1,2,3,4,5)) %>%
  mutate( country = as.character(country) ) %>%
  mutate( Code = as.character(Code) ) %>%
  mutate(Date = mdy(Date)) %>%
  mutate(DeathsPerMillion = as.integer(DeathsPerMillion) - 1) %>%
  mutate(CasesPerMillion = as.integer(CasesPerMillion) - 1)

TidyCOVID

```


```{r}
TidyCOVID

```





```{r}

February1 <-
  TidyCOVID %>%
  filter(Date == "2020-02-01") %>%
  subset(select = c(1,4,5)) %>%
  rename(DeathsPerMillionFeb1 = DeathsPerMillion) %>%
  rename(CasesPerMillionFeb1 = CasesPerMillion)

February29 <- 
  TidyCOVID %>%
  filter(Date == "2020-02-29") %>%
  subset(select = c(1,4,5)) %>%
  rename(DeathsPerMillionFeb29 = DeathsPerMillion) %>%
  rename(CasesPerMillionFeb29 = CasesPerMillion)

FebGrow <-
  full_join(February1, February29, by = c("country")) %>%
  mutate("CasesPerMillionFebGrowth" = CasesPerMillionFeb29 - CasesPerMillionFeb1) %>%
  mutate("DeathsPerMillionFebGrowth" = DeathsPerMillionFeb29 - DeathsPerMillionFeb1)




March1 <-
  TidyCOVID %>%
  filter(Date == "2020-03-01") %>%
  subset(select = c(1,4,5)) %>%
  rename(DeathsPerMillionMarch1 = DeathsPerMillion) %>%
  rename(CasesPerMillionMarch1 = CasesPerMillion)

March31 <- 
  TidyCOVID %>%
  filter(Date == "2020-03-31") %>%
  subset(select = c(1,4,5)) %>%
  rename(DeathsPerMillionMarch31 = DeathsPerMillion) %>%
  rename(CasesPerMillionMarch31 = CasesPerMillion)

MarchGrow <-
  full_join(March1, March31, by = c("country")) %>%
  mutate("CasesPerMillionMarchGrowth" = CasesPerMillionMarch31 - CasesPerMillionMarch1) %>%
  mutate("DeathsPerMillionMarchGrowth" = DeathsPerMillionMarch31 - DeathsPerMillionMarch1)



December31 <-
  TidyCOVID %>%
  filter(Date == "2019-12-31") %>%
  subset(select = c(1,4,5)) %>%
  rename(DeathsPerMillionDecember31 = DeathsPerMillion) %>%
  rename(CasesPerMillionDecember31 = CasesPerMillion)


April5 <-
  TidyCOVID %>%
  filter(Date == "2020-04-05") %>%
  subset(select = c(1,4,5)) %>%
  rename(DeathsPerMillionApril5 = DeathsPerMillion) %>%
  rename(CasesPerMillionApril5 = CasesPerMillion)

TotalGrow <-
  full_join(December31, April5, by = c("country")) %>%
  mutate("CasesPerMillionTotalGrowth" = CasesPerMillionApril5 - CasesPerMillionDecember31) %>%
  mutate("DeathsPerMillionTotalGrowth" = DeathsPerMillionApril5 - DeathsPerMillionDecember31)


```

```{r}


FebGrow <-
  FebGrow %>%
  subset(select = c(1, 6, 7))

MarchGrow <-
  MarchGrow %>%
  subset(select = c(1, 6, 7))

TotalGrow <-
  TotalGrow %>%
  subset(select = c(1, 6, 7))


Growth <-
  left_join(FebGrow,MarchGrow) %>%
  left_join(TotalGrow)

Growth

```










### Data Wrangling of Continents
```{r}
Continents

```

```{r}
TidyCont <-
  Continents %>%
  filter(row_number() > 2) %>%
  subset(select = c(1,23)) %>%
  rename(country = name ) %>%
  mutate( country = as.character(country) ) %>%
  mutate( Continent = as.character(Continent) )

TidyCont


```


```{r}
COVIDwCont <-
  left_join(TidyCOVID, TidyCont, by = "country")
```

## Data Visualization
```{r}
DataMix <- left_join(COVIDwCont, CountryData, by = "country")
```

JOEY: I think this is a good one to start out with, but if there is some way that you could make this more clear (color code, etc.) that would be great.  This is a good start to our data.
```{r}

ggplot(data = DataMix,aes(x = Date, y = CasesPerMillion)) +
  geom_point() +
  facet_wrap(~Continent)


```

JOEY: I like this graph because it visualizes one of the factors that contributes to the development classification.  Can we add in a color code or something that would represent one more factor, such as different countries or regions of the world.  The more that we can do with this one, the better.

```{r}

ggplot(data= DataMix, aes(x= Date, y = DeathsPerMillion)) +
  geom_point() +
  facet_wrap(~Continent)


```





```{r}

GrowthMix <-left_join(Growth, CountryData, by = "country")


```



```{r}
ggplot(data=GrowthMix, aes(x = airports,y = CasesPerMillionTotalGrowth)) +
  geom_point() +
  xlim(0,5000)

```

```{r}

ggplot(data=GrowthMix, aes(x = health,y = CasesPerMillionTotalGrowth)) +
  geom_point()

```
```{r}

ggplot(data=GrowthMix, aes(x = health,y = DeathsPerMillionTotalGrowth)) +
  geom_point()

```

```{r}
GrowthMix %>%
  arrange(desc(CasesPerMillionTotalGrowth))
```



```{r}

ShowChange <-
  GrowthMix %>%
  select(c(1, 6, 7, 9)) %>%
  filter(country != "Africa",
         country != "Asia",
         country != "Europe",
         country != "North America",
         country != "Oceania",
         country != "South America",
         country != "World"
         ) %>%
  mutate("TotalGrowth" = (CasesPerMillionTotalGrowth * round(pop/1000000, digits = 0))) %>%
  mutate("TotalDeath" = (DeathsPerMillionTotalGrowth * round(pop/1000000, digits = 0)))

ShowChange %>%
  arrange(desc(TotalDeath))
```





```{r}

WorldMap(ShowChange, key = country, fill = TotalGrowth)

```



